Method and apparatus for detecting ground attribute of legged robot

ABSTRACT

A method for detecting a ground attribute of a legged robot includes obtaining a collision audio of a foot of the legged robot with a ground; and detecting a workable level attribute of the ground in a working environment of the legged robot according to the collision audio. The sound of the collision between the foot of the robot and the ground is collected, and the workable level attribute of the ground in the working environment of the legged robot is detected based on the sound, so that the operable level attribute can be effectively used to control the legs of the legged robot. On the one hand, the motion noise of the legged robot can be reduced, and on the other hand, the power consumption of the legged robot can be reduced, thereby increasing its range of motion.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is based upon and claims priority to ChinesePatent Application No. 202011389985.3, filed on Dec. 1, 2020, the entirecontents of which are incorporated herein by reference.

FIELD

The present disclosure relates to a field of robotics, and in particularto a method and apparatus for detecting a ground attribute of a leggedrobot.

BACKGROUND

With the continuous development of robot technology, legged robots havebecome more widely used, making the transition from research to home andconsumer use. For example, robot pets such as robot dogs, have appearedto accompany users.

SUMMARY

The present disclosure provides a method and apparatus for detecting aground attribute of a legged robot. The technical solutions of thepresent disclosure are described as follows.

Embodiments of the present disclosure include a method for detecting aground attribute of a legged robot, including: obtaining a collisionaudio of a foot of the legged robot with a ground; and detecting aworkable level attribute of the ground in a working environment of thelegged robot according to the collision audio.

Embodiments of the present disclosure also include a method forcontrolling a legged robot, including: collecting the workable levelattribute of the ground with the method as described above, andcontrolling the legged robot according to the workable level attribute.

Embodiments of the present disclosure also include an apparatus fordetecting a ground attribute of a legged robot, including: one or moreprocessors; a memory storing instructions executable by the one or moreprocessors; in which the one or more processors are configured to:obtain a collision audio of a foot of the legged robot with a ground;and detect a workable level attribute of the ground in a workingenvironment of the legged robot according to the collision audio.

It should be understood that the above general description and thefollowing detailed description are only exemplary and explanatory, andcannot limit the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments consistent with thepresent disclosure and, together with the description, serve to explainthe principles of the present disclosure.

FIG. 1 is a flowchart showing a method for detecting a ground attributeof a legged robot according to an exemplary embodiment;

FIG. 2 is a flowchart showing a method for detecting a ground attributeof a legged robot according to another exemplary embodiment;

FIG. 3 is a flowchart of a method for controlling a legged robotaccording to an embodiment of the present disclosure;

FIG. 4 is a structural diagram of an apparatus for detecting a groundattribute of a legged robot according to an embodiment of the presentdisclosure;

FIG. 5 is a structural diagram of a system for controlling a leggedrobot according to an embodiment of the present disclosure;

FIG. 6 is a schematic diagram of a legged robot according to anembodiment of the disclosure.

DETAILED DESCRIPTION

In order to enable those of ordinary skilled in the art to betterunderstand the technical solutions of the present disclosure, thetechnical solutions in the embodiments of the present disclosure will bedescribed clearly and completely with reference to the accompanyingdrawings.

It should be noted that the terms “first” and “second” in thespecification and claims of the present disclosure and theabove-mentioned drawings are used to distinguish similar objects, andnot necessarily used to describe a specific sequence or sequence. Itshould be understood that the data used in this way can be interchangedunder appropriate circumstances so that the embodiments of the presentdisclosure described herein can be implemented in an order other thanthose illustrated or described herein. The implementation mannersdescribed in the following exemplary embodiments do not represent allimplementation manners consistent with the present disclosure. Rather,they are merely examples of devices and methods consistent with someaspects of the present disclosure as detailed in the appended claims.

In the related arts, robots, especially legged robots, have highrequirements for robot mobility. On the one hand, legged robots arerequired to have strong mobility and a larger range of motion. A largerrange of motion can reduce the power consumption of the legged robot sothat it can support greater mobility. In addition, for the legged robot,its motion noise is also very large for the user. If the drive controlfor the foot or leg of the legged robot is not good, there may be veryloud noises when the legged robot is running, thus affecting the normaluser experience. However, the above-mentioned problems are based on theground attributes of the legged robot. Therefore, effectively detectingthe ground attribute of the working environment where the legged robotis located has become an urgent problem to be solved.

FIG. 1 is a flowchart showing a method for detecting a ground attributeof a legged robot according to an exemplary embodiment. The method fordetecting the ground attribute is used in a legged robot. In theembodiments of the present disclosure, the legged robot may be atwo-legged robot, a quadruped robot, or even a three-legged robot, or amulti-legged robot, and there is no limitation on this. FIG. 1illustrates a flowchart of a method for detecting a ground attribute ofa legged robot in an embodiment of the present disclosure, whichincludes the following steps.

At step 110, a collision audio of a foot of the legged robot with aground is obtained.

In an embodiment of the present disclosure, a microphone may beinstalled in the foot of the legged robot, and the collision audiobetween the foot and the ground can be detected through the microphone.In another embodiment of the present disclosure, a cavity may beprovided at the tail end of the foot of the legged robot, the microphoneis arranged in the cavity, and an end cap is provided to close thecavity. Due to the sound conduction effect, the microphone placed in thecavity can also detect the impact audio. At the same time, the end capof the cavity can also effectively protect the microphone.

In an embodiment of the present disclosure, a microphone may be providedon each foot of the legged robot for detection. Of course, in otherembodiments of the present disclosure, a microphone may be provided onpart of the feet, for example, only provided on a front foot of thelegged robot, not on a rear foot, and only the front foot may be usedfor detection.

At step 130, a workable level attribute of the ground in a workingenvironment of the legged robot is detected according to the collisionaudio.

In the embodiments of the present disclosure, one or more workable levelattributes can be recognized by means of machine learning. The machinelearning algorithm can be Convolutional Neural Network (CNN), RecurrentNeural Network (RNN), Long Short-Term Memory (LSTM), etc. In anembodiment of the present disclosure, the detected collision audio canbe recognized through an image recognition model, thereby generatingworkable level attributes.

In an embodiment of the present disclosure, the collision audio is firstsampled to generate a first collision image, and then the firstcollision image is input to a first neural network model to generate theworkable level attribute.

In this embodiment, filtering processing (for example, high-passfiltering processing) may be performed on the collected collision audioto remove noise therein. After that, the collision audio after filteringthe noise is sampled and subjected to a short-time Fourier transform,and added to a fixed-size image template. In this embodiment, since theshort-time Fourier transform has time information, the frequency valueafter the time-series transformation can be filled into the imagetemplate to obtain the first collision image. Then the first collisionimage is input into the first neural network model to generate theworkable level attribute. The first neural network model can beimplemented through training, for example, the collision audio of thefoot colliding with the ground of various materials may be collected andlabeled. The training of the first neural network model can be completedthrough such training data.

In an embodiment of the present disclosure, the workable level attributecan be divided into multiple levels, and multiple workable levelattributes may be generated by detecting the ground surface material ineach work scene of the legged robot. Specifically, in an embodiment, thefollowing four workable level attributes can be set:

workable level attribute 1: high hardness and non-destructiblematerials, such as metal, stone, wood and polymer materials, etc.;

workable level attribute 2: high hardness but destructible materials,such as glass, plastic and ceramics, etc.;

workable level attribute 3: low hardness, non-destructible anddeformable materials, such as cloth, cotton, thin rubber, sponge, etc.;

workable level attribute 4: low hardness but destructible and deformablematerials, such as cardboard, wet soil, mud and sand, etc.

For each material in the workable level attribute, the correspondingcollision audio can be collected and input into the first neural networkmodel for training. Similarly, for the aforementioned four workablelevel attributes, for legged robots, different workable level attributeshave different mobility capabilities on the legged robot. For example,the lower the level, the stronger the mobility. As for the workablelevel attribute 4, generally speaking, it should be controlled by thelegged robot to avoid it. Legged robots require a certain degree ofhardness and rigidity on the acting ground, which can be used as a rigidbody to support the robot body. Therefore, the legged robots preferablyselect grounds with workable levels attribute of 1-3 to walk.

FIG. 2 is a flowchart showing a method for detecting a ground attributeof a legged robot according to another exemplary embodiment. The methodincludes the following steps.

At step 210, a collision audio of a foot of the legged robot with aground is obtained.

In an embodiment of the present disclosure, a microphone may beinstalled in the foot of the legged robot, and the collision audiobetween the foot and the ground can be detected through the microphone.In another embodiment of the present disclosure, a cavity may beprovided at the tail end of the foot of the legged robot, the microphoneis arranged in the cavity, and an end cap is provided to close thecavity. Due to the sound conduction effect, the microphone placed in thecavity can also detect the impact audio. At the same time, the end capof the cavity can also effectively protect the microphone.

In an embodiment of the present disclosure, a microphone may be providedon each foot of the legged robot for detection. Of course, in otherembodiments of the present disclosure, a microphone may be provided onpart of the feet, for example, only provided on a front foot of thelegged robot, not on a rear foot, and only the front foot may be usedfor detection.

At step 230, a plantar force value of the foot is collected.

In an embodiment of the present disclosure, the plantar force value canbe generated according to a driving current of a driving motor drivingthe foot. In some embodiments, the plantar force value can also becalculated by a driving voltage and other similar parameters.

Specifically, the motor drives the feet through the legs of the leggedrobot. In one embodiment, the driving current of the driving motordriving the foot of the legged robot is first obtained, for example, itcan be detected by a current transformer, or it can also be detected byother means. Then, the plantar force value of the foot is generatedaccording to the driving current.

At step 250, a first collision image is generated by sampling thecollision audio.

In this embodiment, the collision audio is sampled and short-timeFourier transform is performed, and the transform result is added to afixed-size image template. The difference from the previous embodimentis that, the transformation result of the collision audio does not fillthe above-mentioned image template in this embodiment, but some space isreserved for the plantar force value. For example, assuming that a sizeof the fixed-size image template is a 512×512 image, a 1×512 position isreserved for the plantar force value.

At step 270, a second collision image is generated according to theplantar force value and the first collision image.

In an embodiment of the present disclosure, an image sequence isgenerated according to the plantar force value, such as 1×512 data, andthe image sequence is added to the first collision image to generate thesecond collision image. Of course, in other embodiments of the presentdisclosure, other methods may also be used to integrate the plantarforce value into the first collision image.

At step 290, a workable level attribute is generated by inputting thesecond collision image into a first neural network model.

Similarly, in this embodiment, four workable level attributes may alsobe included. The first neural network model can be obtained by trainingin the same manner as described above, and will not be repeated here.

In an embodiment of the present disclosure, in order to improve theaccuracy of collecting collision audio, it is necessary to set a starttime and an end time of the collection. In this embodiment, in responseto receiving a downward extension instruction of a leg associated withthe foot, a microphone is activated to collect the collision audio,which is recorded as the start time of the collection. After a presettime, or in response to receiving a roll-up instruction, the microphoneis controlled to finish the collection, which is recorded as the endtime of the collection.

In an embodiment of the present disclosure, a ground image of the leggedrobot can also be collected through a camera. Through the imagerecognition of the material attributes of the ground, a basis fordecision-making for subsequent robot control can be provided.Specifically, the ground image of the ground is first captured, and thenthe ground image is input to a second neural network to generate amaterial attribute of the ground, the second neural network determinesthe material attribute according to the texture of the ground image. Inthis embodiment, the second neural network can be implemented bytraining. For example, various ground images such as soil, sand, stone,slate, cardboard, soil, rubber, plastic plate, cloth, cotton, and metalplate can be input into the second neural network for training. In anembodiment of the present disclosure, a camera may be provided under thebody of the legged robot, or the camera may be provided in the cavity ofthe foot, for example, the end cap of the cavity may be set to betransparent, so as to collect ground images.

In an embodiment of the present disclosure, it is possible to collect animage of the ground where the foot has already landed, or an image ofthe ground prior to the landing of the foot.

In the embodiments of the present disclosure, the actual applicationscene of the legged robot may include a ground situation that isrelatively complicated. Therefore, the visual recognition result canprovide assistance for the workable level attribute, for example, amaterial attribute may be input into the first neural network model,thereby improving the judgment accuracy and accuracy of the first neuralnetwork model.

As shown in FIG. 3, a flowchart of a method for controlling a leggedrobot according to an embodiment of the present disclosure isillustrated. The method includes following steps.

At step S310, a workable level attribute of a ground is collected.

In an embodiment of the present disclosure, the above-mentioned methodmay be used to collect one or more workable level attributes of theground.

At step S330, the legged robot is controlled according to the attribute.

In the embodiments of the present disclosure, if an attribute of theground has been known, a step frequency, a step height, a body heightand torque output of each motor can be controlled according to theattribute to suit for the current environment, which is conducive to thestability and longevity of the legged robot. In addition, it can alsoreduce the noise of the foot colliding with the ground, and reduce thepower consumption of the foot drive, thereby saving energy consumptionfor the legged robot.

In an embodiment of the present disclosure, a movement path of thelegged robot can be planned according to the detected ground attributes.For example, it is possible to detect the workable level attribute of aroad surface in front of the legged robot, to generate a movement pathaccording to the workable level attribute of the road surface ahead, andcontrol the legged robot to move according to the movement path. In theembodiments of the present disclosure, different grounds have a greatinfluence on the actions of the legged robot. For example, the leggedrobot is most power-saving when walking on the ground with the workablelevel attribute 1. Therefore, the ground with workable level attribute 1can be selected from the front road surface as a footing point of thelegged robot, so as to form a movement path.

In another embodiment of the present disclosure, hazard avoidance canalso be performed based on the detected attributes of the ground onwhich the legged robot has landed. For example, the workable levelattribute of the road surface that the foot of the legged robotcurrently landed is detected, and it is determined whether the workablelevel attribute of the of the road surface that the foot of the leggedrobot currently landed is lower than a preset threshold, and the leggedrobot is controlled to return to a previous position in response to theworkable level attribute being lower than the preset threshold.Specifically, for example, the workable level attribute 4 may cause thefeet of the legged robot to fall into it. Therefore, the presetthreshold can be set to 4. If the legged robot is detected to land onsuch ground, the foot will be controlled to return to the previousposition, thereby avoiding danger and improving the safety performanceof the legged robot.

As shown in FIG. 4, an apparatus for detecting a ground attribute of alegged robot according to an embodiment of the present disclosure isillustrated. The apparatus 400 for detecting a ground attribute of alegged robot includes a collision audio obtaining module 410 and a firstdetecting module 420. In an embodiment of the present disclosure, thecollision audio obtaining module 410 is configured to obtain a collisionaudio of a foot of the legged robot with a ground. In an embodiment ofthe present disclosure, a microphone may be installed in the foot of thelegged robot, and the collision audio between the foot and the groundcan be detected through the microphone. In another embodiment of thepresent disclosure, a cavity may be provided at the tail end of the footof the legged robot, the microphone is arranged in the cavity, and anend cap is provided to close the cavity. Due to the sound conductioneffect, the microphone placed in the cavity can also detect the impactaudio. At the same time, the end cap of the cavity can also effectivelyprotect the microphone. In an embodiment of the present disclosure, amicrophone may be provided on each foot of the legged robot fordetection. Of course, in other embodiments of the present disclosure, amicrophone may be provided on part of the feet, for example, onlyprovided on a front foot of the legged robot, not on a rear foot, andonly the front foot may be used for detection.

In an embodiment of the present disclosure, the first detecting module420 is configured to detect a workable level attribute of the ground ina working environment of the legged robot according to the collisionaudio. First, the collision audio is sampled to generate a firstcollision image, and then the first collision image is input to thefirst neural network model to generate the workable level attribute.

In this embodiment, filtering processing (for example, high-passfiltering processing) may be performed on the collected collision audioto remove noise therein. After that, the collision audio after filteringthe noise is sampled and subjected to a short-time Fourier transform,and added to a fixed-size image template. In this embodiment, since theshort-time Fourier transform has time information, the frequency valueafter the time-series transformation can be filled into the imagetemplate to obtain the first collision image. Then the first collisionimage is input into the first neural network model to generate theworkable level attribute. The first neural network model can beimplemented through training, for example, the collision audio of thefoot colliding with the ground of various materials may be collected andlabeled. The training of the first neural network model can be completedthrough such training data.

In an embodiment of the present disclosure, the workable level attributecan be divided into multiple levels, and multiple workable levelattributes may be generated by detecting the ground surface material ineach work scene of the legged robot. Specifically, in an embodiment, thefollowing four workable level attributes can be set:

workable level attribute 1: high hardness and non-destructiblematerials, such as metal, stone, wood and polymer materials, etc.;

workable level attribute 2: high hardness but destructible materials,such as glass, plastic and ceramics, etc.;

workable level attribute 3: low hardness, non-destructible anddeformable materials, such as cloth, cotton, thin rubber, sponge, etc.;

workable level attribute 4: low hardness but destructible and deformablematerials, such as cardboard, wet soil, mud and sand, etc.

For each material in the workable level attribute, the correspondingcollision audio can be collected and input into the first neural networkmodel for training. Similarly, for the aforementioned four workablelevel attributes, for legged robots, different workable level attributeshave different mobility capabilities on the legged robot. For example,the lower the level, the stronger the mobility. As for the workablelevel attribute 4, generally speaking, it should be controlled by thelegged robot to avoid it. Legged robots require a certain degree ofhardness and rigidity on the acting ground, which can be used as a rigidbody to support the robot body. Therefore, the legged robots preferablyselect grounds with workable levels attribute of 1-3 to walk.

In an embodiment of the present disclosure, the first detecting module410 includes a sampling unit 411 and a first neural network model 412.The sampling unit 411 is configured to generate a first collision imageby sampling the collision audio. The first neural network model 412 isconfigured to generate the workable level attribute according to thefirst collision image.

In an embodiment of the present disclosure, the ground attributedetection device 400 further includes a plantar force collecting module430, configured to collect a plantar force value of the foot.

In an embodiment of the present disclosure, the first detecting module410 further includes an image generating module 413, configured togenerate a second collision image according to the plantar force valueand the first collision image, in which neural network model isconfigured to generate the workable level attribute according to thesecond collision image.

In an embodiment of the present disclosure, the plantar force collectingmodule 430 includes a drive current obtaining unit and a plantar forcevalue generating unit. The driving current obtaining unit is configuredto obtain a driving current of a driving motor driving the foot in thelegged robot. The plantar force value generating unit is configuredgenerate the plantar force value of the foot according to the drivingcurrent.

In an embodiment of the present disclosure, the image generating module413 is configured to generate an image sequence according to the plantarforce value; and generate the second collision image by adding the imagesequence to the first collision image.

In an embodiment of the present disclosure, the apparatus 400 furtherincludes a photographing module 440 and a second detecting module 450.The photographing module 440 is configured to photograph a ground imageof the ground. The second detecting module 450 is configured to generatea material attribute of the ground by inputting the ground image to asecond neural network, wherein the second neural network determines thematerial attribute according to a texture of the ground image.

In an embodiment of the present disclosure, the ground attributedetection device 400 further includes a collection control module 460,configured to activate a microphone to collect the collision audio inresponse to receiving a downward extension instruction of a legassociated with the foot; and control the microphone to finishcollecting the collision audio after a preset time or in response toreceiving a roll-up instruction of the leg.

As shown in FIG. 5, a structural diagram of a system for controlling alegged robot according to an embodiment of the present disclosure isillustrated. The system 500 for controlling a legged robot includes anapparatus 510 for detecting a ground attribute of a legged robot and acontrol device 520. The apparatus 510 may be the above-mentionedapparatus 400. The control device 520 is configured to control thelegged robot according to the ground attribute.

In an embodiment of the present disclosure, the control device 520includes a first detecting module 521, a movement path generating module522, and a first controlling module 523. The first detecting module 521is configured to detect a workable level attribute of a road surface infront of the legged robot. The movement path generating module 522 isconfigured to generate a movement path according to the workable levelattribute of the road surface. The first controlling module 523 isconfigured to control the legged robot is controlled to move accordingto the movement path.

In an embodiment of the present disclosure, the control device 520includes a second detecting module 524 and a second controlling module525. The second detecting module 524 is configured to detect a workablelevel attribute of a current road surface beneath the foot of the leggedrobot. The second controlling module 525 is configured to control thelegged robot to return to a previous position in response to theworkable level attribute of the current road surface being smaller thana preset threshold.

As shown in FIG. 6, a schematic diagram of a legged robot according toan embodiment of the disclosure is illustrated. The legged robot 100includes a head 110, a torso body 120, a leg 130 connected to the torsobody 120, and a foot 140 connected to the leg 130. It also includes theapparatus for detecting a ground attribute of a legged robot asmentioned above, or the system for controlling a legged robot asmentioned above.

In an embodiment of the present disclosure, the foot of the legged robotcomprises a cavity, a microphone is arranged in the cavity, and thecollision audio is collected through the microphone.

In an embodiment of the present disclosure, an apparatus for detecting aground attribute of a legged robot is also proposed, including aprocessor; a memory for storing instructions executable by theprocessor. The processor is configured to execute the instruction toimplement the method for detecting a ground attribute of a legged robotas mentioned above, or the method for controlling a legged robot asmentioned above.

In an embodiment of the present disclosure, a storage medium is alsoproposed. When instructions in the storage medium are executed by aprocessor of an apparatus for detecting a ground attribute of a leggedrobot or a system for controlling a legged robot, the apparatus fordetecting a ground attribute of a legged robot or the system forcontrolling a legged robot is caused to execute the method for detectinga ground attribute of a legged robot as described above or the methodfor controlling a legged robot as described above.

Regarding the device in the foregoing embodiment, the specific manner inwhich each module performs operation has been described in detail in theembodiment of the method, and detailed description will not be givenhere.

With the embodiments of the present disclosure, the sound of thecollision between the foot of the robot and the ground is collected, andthe workable level attribute of the ground in the working environment ofthe legged robot is detected based on the sound, so that the operablelevel attribute can be effectively used to control the legs of thelegged robot. On the one hand, the motion noise of the legged robot canbe reduced, and on the other hand, the power consumption of the leggedrobot can be reduced, thereby increasing its range of motion.

Those skilled in the art will easily think of other embodiments of thepresent disclosure after considering the specification and practicingthe invention disclosed herein. This application is intended to coverany variations, uses, or adaptive changes of the present disclosure.These variations, uses, or adaptive changes follow the generalprinciples of the present disclosure and include common knowledge orconventional technical means in the technical field that are notdisclosed in the present disclosure. The description and the embodimentsare to be regarded as exemplary only, and the true scope and spirit ofthe present disclosure are defined by the following claims.

It should be understood that the present disclosure is not limited tothe precise structure that has been described above and shown in thedrawings, and various modifications and changes can be made withoutdeparting from its scope. The scope of the present disclosure is onlylimited by the appended claims.

What is claimed is:
 1. A method for detecting a ground attribute of a legged robot, comprising: obtaining a collision audio of a foot of the legged robot with a ground; and detecting a workable level attribute of the ground in a working environment of the legged robot according to the collision audio.
 2. The method of claim 1, wherein detecting the workable level attribute of the ground in the working environment of the legged robot according to the collision audio comprises: generating a first collision image by sampling the collision audio; and generating the workable level attribute by inputting the first collision image into a first neural network model.
 3. The method of claim 2, wherein generating the workable level attribute by inputting the first collision image into the first neural network model comprises: collecting a plantar force value of the foot; and generating a second collision image according to the plantar force value and the first collision image, so as to input the second collision image to the first neural network model.
 4. The method of claim 3, wherein collecting the plantar force value of the foot comprises: obtaining a driving current of a driving motor driving the foot in the legged robot; and generating the plantar force value of the foot according to the driving current.
 5. The method of claim 3, wherein generating the second collision image according to the plantar force value and the first collision image comprises: generating an image sequence according to the plantar force value; and generating the second collision image by adding the image sequence to the first collision image.
 6. The method of claim 1, further comprising: photographing a ground image of the ground; and generating a material attribute of the ground by inputting the ground image to a second neural network, wherein the second neural network determines the material attribute according to a texture of the ground image.
 7. The method of claim 1, wherein the foot of the legged robot comprises a cavity, a microphone is arranged in the cavity, and the collision audio is collected through the microphone.
 8. The method of claim 1, wherein obtaining the collision audio of the foot of the legged robot with the ground comprises: activating a microphone to collect the collision audio in response to receiving a downward extension instruction of a leg associated with the foot; and controlling the microphone to finish collecting the collision audio after a preset time or in response to receiving a roll-up instruction of the leg.
 9. A method for controlling a legged robot, comprising: collecting a workable level attribute of a ground by obtaining a collision audio of a foot of the legged robot with the ground; and detecting the workable level attribute of the ground in a working environment of the legged robot according to the collision audio; and controlling the legged robot according to the workable level attribute.
 10. The method of claim 9, wherein controlling the legged robot according to the workable level attribute comprises: detecting a workable level attribute of a road surface in front of the legged robot; generating a movement path according to the workable level attribute of the road surface; and controlling the legged robot is controlled to move according to the movement path.
 11. The method of claim 9, wherein controlling the legged robot according to the workable level attribute comprises: detecting a workable level attribute of a current road surface beneath the foot of the legged robot; determining whether the workable level attribute of the current road surface is smaller than a preset threshold; and controlling the legged robot to return to a previous position in response to the workable level attribute of the current road surface being smaller than the preset threshold.
 12. An apparatus for detecting a ground attribute of a legged robot, comprising: one or more processors; a memory storing instructions executable by the one or more processors; wherein the one or more processors are configured to: obtain a collision audio of a foot of the legged robot with a ground; and detect a workable level attribute of the ground in a working environment of the legged robot according to the collision audio.
 13. The apparatus of claim 12, wherein the one or more processors are configured to: generate a first collision image by sampling the collision audio; and generate the workable level attribute according to the first collision image.
 14. The apparatus of claim 13, wherein the one or more processors are configured to: collect a plantar force value of the foot; and generate a second collision image according to the plantar force value and the first collision image, wherein the first neural network model is configured to generate the workable level attribute according to the second collision image.
 15. The apparatus of claim 14, wherein the one or more processors are configured to: obtain a driving current of a driving motor driving the foot in the legged robot; and generate the plantar force value of the foot according to the driving current.
 16. The apparatus of claim 14, wherein the one or more processors are configured to: generate an image sequence according to the plantar force value; and generate the second collision image by adding the image sequence to the first collision image.
 17. The apparatus of claim 12, wherein the one or more processors are configured to: photograph a ground image of the ground; and generate a material attribute of the ground by inputting the ground image to a second neural network, wherein the second neural network determines the material attribute according to a texture of the ground image.
 18. The apparatus of claim 12, wherein the one or more processors are configured to: activate a microphone to collect the collision audio in response to receiving a downward extension instruction of a leg associated with the foot; and control the microphone to finish collecting the collision audio after a preset time or in response to receiving a roll-up instruction of the leg.
 19. The apparatus of claim 12, wherein the apparatus is comprised in a system for controlling the legged robot, and the system comprises a control device configured to control the legged robot according to the ground attribute.
 20. The apparatus of claim 19, wherein the control device is configured to detect a workable level attribute of a road surface in front of the legged robot; generate a movement path according to the workable level attribute of the road surface; and control the legged robot is controlled to move according to the movement path. 